def executeCLI(commandString): print() print("Executing: {0}".format(commandString)) print() splitArgs = [item.strip() for item in commandString.split(" ")] cliHandler(splitArgs) #Run the command print()
def detect(): if darkflow.net.yolov2.predict.detection_choice == 1: arg = [ 'flow', '--imgdir', './static/', '--model', './cfg/fingertip-yolo.cfg', '--load', '-1', '--batch', '1', '--threshold', '0.5' ] elif darkflow.net.yolov2.predict.detection_choice == 2: arg = [ 'flow', '--imgdir', './static/', '--model', './cfg/pupils_yolo.cfg', '--load', '-1', '--batch', '1', '--threshold', '0.9' ] cliHandler(arg)
def upload_file(request): if request.method == 'POST': form = PhotoForm(request.POST, request.FILES) if form.is_valid(): form.save() else: form = PhotoForm() image_name = request.FILES['photo'] return HttpResponse(cliHandler(image_name))
def upload_file(request): if request.method == 'POST': form = PhotoForm(request.POST, request.FILES) if form.is_valid(): form.save() return render(request, 'polls/index.html', {'form': form}) else: form = PhotoForm() image_name = request.FILES['photo'] return HttpResponse(cliHandler(image_name))
def classify(fileName, decimg): cliHandler(sys.argv) openFile = open(fileName) try: data = json.load(openFile) except: openFile = open("./sample_img/out/none.json") data = json.load(openFile) retStr = '' for i in range(len(data)): if (data[i]["confidence"] >= 0.6): retStr += data[i]["label"] + " " topLeft_x = data[i]["topleft"]["x"] topLeft_y = data[i]["topleft"]["y"] bottomRight_x = data[i]["bottomright"]["x"] bottomRight_y = data[i]["bottomright"]["y"] cv2.rectangle(decimg, (topLeft_x, topLeft_y), (bottomRight_x, bottomRight_y), (0, 0, 255), 7) return retStr, decimg
def upload_file(request): if request.method == 'POST': form = PhotoForm(request.POST,request.FILES) if form.is_valid(): form.save() print(form) # return render(request, 'polls/index.html', {'form': form}) else: form =PhotoForm() image_name = request.FILES['photo'] fname = 'C:/img/'+str(image_name) im = Image.open(fname) im.save('./polls/static/before.jpg') # im.show() data, ingre = cliHandler(image_name) # pd.set_option('colheader_justify', 'center') # return HttpResponse(cliHandler(image_name)) return render(request, 'polls/upload_file.html')
import sys from darkflow.cli import cliHandler cliHandler(sys.argv)
def flow_data(): image_name = get_image() cliHandler() save_data(parse_json(image_name)) cleanup(image_name)
def _execute_yolo(commandstring): command = [""]+commandstring.split(" ") print ("executing command",command) cliHandler(command)
#args.append("bin/yolov2-tiny-voc.weights") args.append("-1") args.append("--train") args.append("--annotation") args.append("train/annotation") args.append("--dataset") args.append("train/ImgPlates") args.append("--summary") args.append("summary/") args.append("--epoch") args.append("100") args.append("--batch") args.append("16") print("\n\n\n") for i in args: print(i, end=" ") print("\n\n\n") from darkflow.cli import cliHandler cliHandler(args)